How on-farm AI can help horticulture supply chains


Has there ever been a more turbulent time for horticulture supply chains than the last two and a half years?  At times there’s been shortages of berries on supermarket shelves, we’ve been urged to eat more avocados when producers couldn’t move them quick enough and who can forget the highly publicised lettuce shortage?

Prolonged La Niña rainfall and extreme flooding events continue to impact the production and distribution of crops in East Coast growing regions. The resulting inconsistency in stock has caused highly volatile prices for consumers.  Inflated costs of core inputs like fuel, fertiliser, and chemicals have also added to the complexity of decision making and made planning on-farm production much harder this season.

Food Agility CRC and its partners are investing more than $5 million in a range of projects through the AI and Robotics Pillar, to develop technologies critical in helping businesses manage some of these challenges. While we certainly can’t control the weather, what we’re finding is that technology can unlock previously hidden avenues for productivity gains and efficiency, along with improved prospects for key issues such as sustainability and animal welfare in farming management.

Let’s focus on Artificial Intelligence (AI), where one of the most common applications in agriculture is in Machine Learning (ML). ML draws on modern computing power to process and analyse multi-dimensional data in new ways, creating opportunities to use farming data for significant forecasting and planning benefits. Whilst individual technology can add some benefit, typically we see the real power of AI and ML revealed once we combine farming datasets.

Technology can unlock previously hidden avenues for productivity gains and efficiency in farming systems. (Image of Green Atlas Cartographer supplied by Agriculture Victoria)

Food Agility projects are examining how information like climate and weather records, remote sensing data from satellites, real-time animal monitoring tags, and records of farm management practices can be combined to generate both accurate historical analysis and increasingly accurate future predictions of farm yields and performance.  

In this research, we’re partnering with both Australian and multinational technology providers who already have tried and tested technology operating in the field. We’re bringing them together with industry and researchers to find solutions to real-world challenges, fill gaps in technology and explore opportunities to leverage more value.  

For example, Food Agility is working with The Yield Technology Solutions, Yamaha, UTS and Treasury Wine Estates in a project combining robotics and micro-climate weather services to improve the accuracy of wine grape harvest predictions. This will improve the accuracy of decision making both on-farm and in post-farm processing, contributing to a more efficient and sustainable wine sector.

The development of this technology is not without its difficulties. Along with the functional challenges posed by collating data from multiple sources, there are also important technical barriers we face such as issues in regional connectivity, data ownership, standards and interoperability, and data privacy and security, each of which Food Agility is looking to address in other projects and related initiatives. Our partnership with Bosch Global and UTS to ensure the safe storage and sharing protection of farm data is one exciting example.

The biological and ecological complexities of food production systems mean that there is still a long way to go before we can generate prediction tools with the precision of a crystal ball. However, we’re already finding value being unlocked for producers and processors, particularly in planning farm operations and labour, scheduling of post-farm logistics and improved financial forecasting for agribusinesses.

Identifying key inputs, processes and outputs is a great place for producers to start when it comes to digitalisation

Data is the lifeblood for Machine Learning, and we’re seeing members of our industry are at different stages of their digitalisation roadmaps, so for those looking to start the journey towards AI-powered farming, a great first step is to identify the key inputs, processes and outputs of your farming business. Then for each of those, take stock of what data you are currently collecting, where this data is stored, if it can be easily accessed electronically, and if there are any key gaps.

Those gaps could be plugged by investing into new sensors or monitoring technologies to make sure key inputs and outputs are being captured digitally. It will also be useful to think about the level of resolution that your data should be at in order to be useful for you and your business.

For example, if you’re a horticultural grower thinking about using AI for harvest yield predictions in the future, a key enabler will be having a solid record of previous yields at the level of granularity you wish to predict at. An upfront investment in equipment that helps record inputs and accurate yield tonnage volumes at harvest would be an essential starting point.

As more digital tools are implemented on farm, data can unlock more pieces of the farming system puzzle. This will allow farmers to manage their production with greater insight and confidence in decision making.  It’s certainly exciting to see how data and information technology systems can contribute to more resilient food and agricultural systems for the future.  

About the author

‍Ashley Rootsey leads the AI & Robotics Pillar at Food Agility CRC and is a graduate of the University of Sydney's B. Food Science and Agribusiness (Hons) program. He’s passionate about combining modern innovation methods with the unique challenges and opportunities presented in Australian agrifood.

Full article can be found on the Food Agility CRC website.

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